The gut microbiome is linked with obesity through altering nutrient absorption, pathogen resistance and inflammation through modulating the immune system, as well as neurodegenerative diseases through endocrinological signalling pathways. As such, meta-proteomics aims to study the proteome of these gut microbes in their host environment, which has been shown to be crucial for our understanding of human health.
However, analysing meta-proteomic data requires the capability to handle large-scale datasets containing qualitative and quantitative information of proteins identified from highly redundant databases. \Although many approaches are available for conventional proteomics analysis, these still have difficulties processing large chimeric databases for meta-proteomics studies. To address these issues, we established a workflow combining quantitative proteomics methods such as tandem mass tag (TMT) quantification or label-free quantification (LFQ) with sample-specific databases refined from both the host as well as the microbes. This significantly reduces analysis time without sacrificing the quality of the outcome of a meta-proteomics study. Herein we present two representative case studies displaying our capabilities in human meta-proteomics with an example of label-free and label-based workflows.